Research Assistant, Non-Communicable Disease Epidemiology, Modelling Metrics and Digital Health
1 day left
- Full Time
Applications are invited for the following full-time position in the Saw Swee Hock School of Public Health:
Research Assistant x 1 (1 year minimum, up to 3 years)
A research assistant position is available in the Saw Swee Hock School of Public Health to support a Principal Investigator in research projects in the areas of non-communicable disease (cardiovascular) epidemiology, Modelling Metrics and digital health.
Major duties and responsibility of the post includes managing, extracting, analysing and document data from databases or raw datasets. In addition, candidate is required to do report writing, presentations, and publication of results and findings in peer-reviewed journals. Furthermore, he/she will need to contribute to development and writing of research grant proposals.
Candidate must be an independent mature worker who is well-organized and has an eye for details.
Candidate must possess excellent written and verbal communication skills. He/she must possess the ability to work effectively with colleagues to achieve team goals. Good proficiency in the following areas is essential:
- Statistical software (R, Stata or Python).
- Microsoft Office Applications, particularly MS Word, Excel (preferably with proficiency in Visual Basic)
Good proficiency and experience in the following areas is preferred:
- R (preferred), Stata or Python
- Scientific writing
- Survey design
- Desirable: qualitative research
- Desirable: knowledge of reading ECG’s or willingness to learn
Recruitment is open immediately and will continue until the position is filled. Applicants should send a brief statement of interest, CV, three named references, and other supporting documents during the application.
We regret that only shortlisted candidates will be notified.
The successful applicant is expected to possess at least a Bachelor’s or Master’s Degree in Statistics, or a Master’s degree in Public Health with a strong quantitative background. Alternatively, Bachelor’s degree in computer science or Mathematics, with relevant experience in data science may be considered.